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You Can Judge an Artist by an Album Cover: Using Images for Music Annotation

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You Can Judge an Artist by an Album Cover: Using Images for Music Annotation You Can Judge an Artist by an Album Cover: Using Images for Music Annotation Janis¯ L¯ıbeks Douglas Turnbull Swarthmore College Department of Computer Science Swarthmore, PA 19081 Ithaca College Email: [email protected] Ithaca, NY 14850 Email: [email protected] Abstract—While the perception of music tends to focus on our acoustic listening experience, the image of an artist can play a role in how we categorize (and thus judge) the artistic work. Based on a user study, we show that both album cover artwork and promotional photographs encode valuable information that helps place an artist into a musical context. We also describe a simple computer vision system that can predict music genre tags based on content-based image analysis. This suggests that we can automatically learn some notion of artist similarity based on visual appearance alone. Such visual information may be helpful for improving music discovery in terms of the quality of recommendations, the efficiency of the search process, and the aesthetics of the multimedia experience. Fig. 1. Illustrative promotional photos (left) and album covers (right) of artists with the tags pop (1st row), hip hop (2nd row), metal (3rd row). See I. INTRODUCTION Section VI for attribution. Imagine that you are at a large summer music festival. appearance. Such a measure is useful, for example, because You walk over to one of the side stages and observe a band it allows us to develop a novel music retrieval paradigm in which is about to begin their sound check. Each member of which a user can discover new artists by specifying a query the band has long unkempt hair and is wearing black t-shirts, image. Second, images of artists also represent an unexplored black boots, and tight black jeans with studded black leather source of music information that is useful for the automatic belt. It would be reasonable to expect that they play heavy annotation of music: associating semantic tags with artists metal. Furthermore, it would be natural for you to check out [17]. Once annotated, an artist can be retrieved using a text- a different stage before you hear them play a single note if based query much like web pages are retrieved when using a you are not a fan of heavy metal music. This suggests that typical Internet search engine (e.g., Yahoo!, Google). Finally, the outward appearance, or image, of an artist can play a role music-related images provide us with meaningful visual repre- in how their music is received by audiences. Whether this sentation of sound. This is important when considering that it image is carefully constructed by a public relations consultant requires much less time to browse a large collection of images or results from unintentional lifestyle habits of the performer, than to listen to a few short clips of music. it encodes valuable information that helps place the artist into In this paper, we describe an image annotation system that a musical context. can both compute artist similarity and annotate artists with a To this end, we are interested in exploring the relationship set of genre tags based on album cover artwork or promotional between music and music-related images (e.g., album cover photographs. Our system is based on a recently-proposed artwork & promotional photographs of artists - see figure 1) baseline approach called Joint Equal Contribution (JEC) [12]. for a number of reasons. Currently, popular music discovery JEC incorporates multiple forms of low-level color and texture engines, such as Pandora1 and Last.fm2, rely on such images information and has been shown to outperform numerous state- to make their web and mobile applications visually appealing. of-the-art approaches on standard benchmark image data sets. That is, streaming both music and associated images provide In order to use this approach of artist annotation, we modify a listener with an engaging multimedia experience. it in a straight-forward manner so that we can use multiple While improving aesthetics is important for music discovery images per artist to significantly improve performance. [7], our work focuses on using techniques from computer II. RELATED WORK vision to make additional use of music-related images. First, we propose a new measure of music similarity based on visual Over the last decade, there has been a growing interest in developing techniques for both computing music similarity and 1http://www.pandora.com for annotating music with tags [5]. This body of work focuses 2http://last.fm on content-based audio analysis, as well as using other sources Genre Tags of music information, such as social tags, music scores, lyrics, Artists closest to electronic dance pop house classical web documents, and preference data (e.g. [18], [3], [10]). Daft Punk To the best of our knowledge, music-related images, such as Astral Projection 1 0 0 0 0 album covers and promotional photos, have not been used for Deadmau5 1 1 0 1 0 Eurythmics 1 1 1 0 0 these tasks. However, computer vision has been employed for Einsturzende¨ 1 0 0 0 0 other tasks such as optical music recognition [1], identifying Neubauten Tags Predicted documents with music notation [2], and identifying lyrics in 1.0 0.5 0.25 0.25 0.0 using JEC scores [4]. In addition, standard computer vision techniques Real Tags for 1 1 0 1 0 have been applied to 2-D representations (e.g., spectrograms) Daft Punk of audio content for music identification and fingerprinting [9]. TABLE I Within the extensive computer vision literature, there are IMAGE-BASED ARTIST ANNOTATION USING TAG PROPAGATION.FOR A two general tasks that are related to our work. First, content- GIVEN SEED ARTIST (E.G.DAFT PUNK), WE RETRIEVE THE GROUND TRUTH TAG ANNOTATION VECTORS FOR THE ARTISTS WITH THE MOST based image retrieval (CBIR) involves computing similarity SIMILAR IMAGES (E.G., ASTRAL PROJECTION,DEADMAU5, ...) AND THEN between pairs of images. Deselaers et al. [6] provide a recent AVERAGE THEIR ANNOTATION VECTORS TO CALCULATE A PREDICTED survey of CBIR research and describe a number of useful ANNOTATION VECTOR. image features, many of which are used in this paper. The second relevant task is image annotation. For this task, the goal is to annotate an image with a set of tags (e.g., “sky”, averaging the response angles of the 126x126 image over “polar bear”, “forest”). Makadia et al. [12] recently proposed non-overlapping blocks of size 14x14, quantizing to 8 values a system that combines color and texture features using Joint and concatenating the rows of each of the twelve resulting Equal Contribution (JEC) as a baseline approach for this task. 9x9 images, resulting in a 972-dimensional feature vector. We However, the authors unexpectedly found that this approach compute both Gabor and GaborQ distances for each pair of performs better than a number of (more complex) systems.We images by calculating L1-distance. use JEC as the core of our artist annotation system but extend For the Haar features, we take the three Haar filters at three it to use multiple images of each artist. different scales, and convolve them with a (downsampled) IMAGE SIMILARITY 16x16 pixel grayscale version of the image. The simple con- catenation of the response image, a 2304-dimensional vector, To compute image similarity between two images using was called Haar. A second quantized version, referred to as JEC, we first compute seven separate distances between each HaarQ is found by changing each image response value to pair of images. 1, 0 or -1 if the initial response value is positive, zero, or A. Image Features negative, respectively, again, producing a 2304-dimensional vector. Again, we calculate the Haar and HaarQ distance by The first three distances are related to color information. computing the L1-distance between pairs of Haar and pairs of For each image, we compute one color histogram over each HaarQ vectors. of three color spaces: red-green-blue (RGB), hue-saturation- value (HSV), and LAB. The three color histograms are 3- B. Joint Equal Contribution dimensional histograms extracted on 16 equally spaced bins To combine the distances of the seven features using JEC, for each color channel. The interval of the bins is determined we normalize the distances for a feature by the maximum from the possible range of values for each channel in each distance between any pair of images. This results in image- of the respective color space. Each color histogram is rep- image distances in the range [0,1], where 0 denotes that the 3 resented as a 16 = 4096 dimensional feature vector where two images are the same, 1 denotes the most dissimilar pair each element of the vector represents the (normalized) count of images. To combine feature vectors, we average the seven of pixels that fall into a color bin. As in Makadia et al., normalized distances over each pair of images. Note that JEC we calculate the L1-distance when comparing two RGB or is a relatively simple approach because it does not require us two HSV histograms, and calculate the KL-divergence when to learn the parameters of (often complex) parametric models comparing two LAB histograms. as is common in other approaches to image annotation. This The other four distances are related to two types of texture makes the algorithm relatively easy to implement and fast to information: Gabor and Haar features. For the Gabor features, compute. a grayscale version of the image is convolved with complex Gabor wavelets at three scales and four orientations to create III. ARTIST ANNOTATION EXPERIMENTS 12 response images. A histogram of response magnitudes is In this section, we explore image-based music similarity performed using 16 equally-spaced bins with experimentally- by considering the problem of annotating artists with genre determined maxima values.
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